Towards the comparative analysis of the case studies: operative steps Carlo Giupponi 1,2 and Gretel Gambarelli 2,3 1 Università degli Studi di Milano 2.

Towards the comparative analysis Towards the comparative analysis of the case studies: of the case studies: operative stepsoperative stepsCarlo Giupponi1,2 and Gretel Gambarelli2,3

1Università degli Studi di Milano 2FEEM3 PhD, Università Ca’ Foscari di Venezia

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“Cooking” a comparative analysis

5 very different CS

Models inputs Models outputs

Metadata (WP04)3 scenarios

Policy responses

The ingredients

COMPARATIVE ANALYSIS

The dish

DPSIR framework Sustainability indicators

The receipt

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How to cook this dish?

Option 1

- more rigorous

- more ambitious

Option 2

- less rigorous

- less ambitious

The difference between option 1 and 2 is about the relationship between scenarios and responses and the number of necessary models runnings

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WP10 objectives

To identify commonalities and differences and relate them to the specific regional setting;

To identify more generally applicable results that are invariant across the case studies;

To organize these finding in terms of a comparative policy assessment (existing and desirable, future ones) and best practice examples – contribution to sustainability.

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OPTION 1: 5 OPERATIVE STEPS

1) Definition of scenarios

2) Definition of responses (E,F)

3) Definition of sustainability indicators

4) We run the 3 scenarios with existing responses

5) We run the 3 scenarios with desirable future responses

CA on existing policies for each scenario

CA on proposed policies for each scenario

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OPTION 1: step 1

1) Scenarios are defined by COMMON VARIABLES representing DRIVING FORCES of all CS (Climate, Population, Land Use), NOT INCLUDING WATER POLICY RESPONSES.

Precipitation EEA D04.01

Temperature SMART D04.01

Population growth rate UNEP/MAP D04.01

Urban population UNEP/MAP D04.01

Rural population UNEP/MAP D04.01

Population density UNEP/MAP D04.01

Share of Urban area SMART% of total area LUC MODEL

Share of irrigated agricultural land UNEP/MAP

% of total area LUC MODEL

Share of Industrial area SMART% of total area LUC MODEL

Share of Portual area SMART% of total area LUC MODEL

Share of Tourism development area SMART

% of total area LUC MODEL

or: Number of turists per km of coastline UNEP/MAP turists/km2

national statistics

SOURCEPROPOSED

UNIT (2)TYPE INDICATORPROPOSED

BY

CLIMATE(D)

POPULATION (D)

LAND-USE(D)

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OPTION 1: STEP 2

2) Responses are organized in COMMON CATEGORIES for all CS (Water Demand, Water Supply, Water Quality), but single responses are SPECIFIC per CS (PARTICIPATION OF STAKEHOLDERS).

TYPE RESPONSEPROPOSED BY

Water demand management Water prices (domestic, agriculture, industry, tourism) EEAWater subventions SMARTWater distribution and use systems investments SMARTChange in irrigation systems SMARTChange in cropping patterns SMARTRising awareness for limiting abstraction SMARTMinimum flow for environmental purposes SMART

Water supply management Efficiency of water use EEAEfficiency in irrigation UNEP_MAPEfficiency in urban network UNEP_MAPWater leakage EEA

Water harvesting (lakes, reservoirs, small dams) SMART

Reservoir storage investments SMART

Groundwater exploitation SMARTMobilization of surface water SMARTBasin-out water supply (groundwater) SMARTWater imports SMARTRecycling of wastewater SMARTDesalination SMARTLimits to groundwater exploitation SMART

Water quality management Share of industrial wastewatertreated on site UNEP-MAPSolid waste management for avoiding illegal discharge in waterflows SMARTUrban waste water treatment EEAWater treatment investments SMARTShare of collected and treated wastewater by the public sewerage system UNEP-MAPRising awareness for limiting fertilization

SMARTLimit salinization through drainage systems SMARTExistence of monitoring programs concerning pollutants inputs UNEP-MAPNational regulations on wastewater SMART

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OPTION 1: STEP 3

3) Indicators for the CA are COMMON to all CS and address the 3 pillars of sustainability (Economy, Society, Environment) + cross-cutting themes

IMPACT INDICATORS:

D/S ratio for agriculture SMART % WaterWare

or: GDP from agriculture SMARTthousands euros

D/S ratio for industry SMART % WaterWare

or: GDP from industry SMARTthousands euros

D/S ratio for tourism SMART % WaterWare

or: GDP from turistic sector SMARTthousands euros

Economic efficiency of the system SMARTeuros/ mc H2O WaterWare

IMPACT INDICATORS:D/S ratio for domestic uses SMART % WaterWareor: number of days without drinking water SMART days/year WaterWare

IMPACT INDICATOR:D/S ratio for environmental uses SMART %

STATE INDICATORS: Nutrients in coastal waters EEA Telemac

Hazardous substances in transitional, coastal and marine waters EEA Telemac

or: Global quality of coastal waters UNEP - MAP class (I-IV) Telemac

or: Bathing water quality EEA class (I-IV) Telemac

PRESSURE INDICATOR: Water exploitation index (WEI) EEA and

UNEP-MAP: Mean annual total abstraction of freshwater / long-term average freshwater

%

CROSSCUTTING

SOURCE

ECONOMIC

SOCIAL

ENVIRONMENTAL

TYPE INDICATORPROPOSED

BY:PROPOSED

UNIT

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OPTION 1: STEP 4

4) Models are first run for the 3 scenarios, with the CURRENT RESPONSES for all CS. Values of sustainability indicators are derived.

The COMPARATIVE ANALYSIS assesses how current responses perform in different case studies in each scenario.

Policy questions to be answered:

How effective are existing water policies with respect to the management of water supply, water demand and water quality?

What are the current effects of existing water policies on economic performances, the quality of life, the environmental quality?

Are the abstractions from our water resources sustainable over the long term?

What are the differences and communalities in current practices of the 5 CS?

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WP10: STEP 5

5) Models are run PER EACH SCENARIO, PER EACH CATEGORY OF RESPONSES. Each response impacts on a pressure or a state indicator, thus modifying models’ inputs. Values of sustainability indicators are derived.

The COMPARATIVE ANALYSIS assesses how common types of future responses perform in different case studies in each scenario.

Policy questions to be answered:

How effective are proposed water policies with respect to the current practices in improving the management of water supply, water demand and water quality?

How effective are proposed water policies with respect to the current practices in improving economic performances, the quality of life, the ecological quality?

Are the abstractions from our water resources sustainable over the long term if the proposed policies are implemented?

What are the differences and communalities in proposed practices of the 5 CS?

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OPTION 1: MODELS RUNNING

3x4 = 12 runnings of models per each CS

12 different results registered by sustainability indicators

3 scenarios, 1 Existing +3 Future Responses (WD, WS, WQ)

Hence, for each CS:

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OPTION 1: pros and cons

PROS:

- there is a LOGICAL DISTINCTION between external variables (i.e. climate conditions, population growth, etc.) and decision variables (i.e. water policies).

- more consistent with DPSIR: D define scenarios, for each scenario we have different effects on P,S,I indicators and R try to improve P, S, I indicators

CONS:

- rather complex

- many models runnings

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WP10: EXAMPLE

EXAMPLE:

Evaluation of one sustainability indicator (D/S ratio for agriculture):

• 1 scenario (pessimistic)

• 1 variable defining scenario (share of irrigated agricultural land)

• 1 type of response (water demand management. In particular: sprinkler irrigation)

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DF: Increased share of irrigated agricultural land

PESSIMISTIC SCENARIO

INDICATOR BASELINE BAU OPT PESSShare of irrigated area 50% 0% -3% +5%

LUC MODEL

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P: Increase in water demand for agriculture

DF: Increased share of irrigated agricultural

land

P m3/yearWater demand for agriculture

Current irrigation methods, crops etc.

Possibilities for the derivation of sectoral water demand:- water demand derived through a decision table having land use and population growth as inputs- direct derivation of water demands (coherent with land-use).In both cases the sum of sectoral water demands should be equal to the regional water demand for each scenario, as calculated by the LUC model.

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P: Water demand for agriculture

DF: Increase in irrigated surface

S: Total water availability for

agriculture

WATER RESOURCES MANAGEMENT MODEL

S Total water availability for agriculture

m3/y

Aggregation of daily data

Allocation strategies & other inputs

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I: D/S ratio in agriculture

P: Water demand for agriculture

DF: Increase in irrigated surface

S: Total water availability for

agriculture

S Total water availability MC/y

WATER RESOURCES MANAGEMENT MODEL

I D/S ratio in agriculture %

PWater demand for agriculture

MC/Y

Input for CA of existing responses

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P: Increase in Water demand for agriculture

DF: Increase in irrigated surface

S: total water availability for

agriculture

I: D/S ratio in agriculture decreases

P: Increase in Water demand for agriculture

Input for CA of future WDM responses

R: Sprinkler use

P: Decrease in Water demand for agriculture

S: Total water availability for

agriculture unchanged

I: D/S ratio in agriculture improves

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OPTION 2: OPERATIVE STEPS

1) Definition of scenarios, including responses

2) Definition of sustainability indicators

4) BAU scenario (including existing responses)

6) Optimistic scenario (including desirable future responses)

Answer to policy questions

6) Pessimistic scenario (including undesirable future responses)

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OPTION 2: pros and cons

CONS:

- NO LOGICAL DISTINCTION between external variables (i.e. climate conditions, population growth, etc.) and decision variables (i.e. water policies).

- less consistent with DPSIR: D and R are mixed in defining scenarios, so the effect of R on P,S.I indicators is less transparent because other variables (climate, population, etc.) change at the same time

PROS:

- less complex

- less models runnings

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Discussion….

For both option 1 and option 2 we have to agree on

- scenarios

- responses (included or not in scenarios)

- sustainability indicators

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1) SCENARIOS

Precipitation EEA D04.01

Temperature SMART D04.01

Population growth rate UNEP/MAP D04.01

Urban population UNEP/MAP D04.01

Rural population UNEP/MAP D04.01

Population density UNEP/MAP D04.01

Share of Urban area SMART% of total area LUC MODEL

Share of irrigated agricultural land UNEP/MAP

% of total area LUC MODEL

Share of Industrial area SMART% of total area LUC MODEL

Share of Portual area SMART% of total area LUC MODEL

Share of Tourism development area SMART

% of total area LUC MODEL

or: Number of turists per km of coastline UNEP/MAP turists/km2

national statistics

SOURCEPROPOSED

UNIT (2)TYPE INDICATORPROPOSED

BY

CLIMATE(D)

POPULATION (D)

LAND-USE(D)

TELEMAC:- sources of pollution

- type of pollution

- concentration of pollution

WATERWARE: Metadata (WP04)?

- Income increase per sector

- Per capita water consumption by sector, etc.

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2) RESPONSES

TYPE RESPONSEPROPOSED BY

Water demand management Water prices (domestic, agriculture, industry, tourism) EEAWater subventions SMARTWater distribution and use systems investments SMARTChange in irrigation systems SMARTChange in cropping patterns SMARTRising awareness for limiting abstraction SMARTMinimum flow for environmental purposes SMART

Water supply management Efficiency of water use EEAEfficiency in irrigation UNEP_MAPEfficiency in urban network UNEP_MAPWater leakage EEA

Water harvesting (lakes, reservoirs, small dams) SMART

Reservoir storage investments SMART

Groundwater exploitation SMARTMobilization of surface water SMARTBasin-out water supply (groundwater) SMARTWater imports SMARTRecycling of wastewater SMARTDesalination SMARTLimits to groundwater exploitation SMART

Water quality management Share of industrial wastewatertreated on site UNEP-MAPSolid waste management for avoiding illegal discharge in waterflows SMARTUrban waste water treatment EEAWater treatment investments SMARTShare of collected and treated wastewater by the public sewerage system UNEP-MAPRising awareness for limiting fertilization

SMARTLimit salinization through drainage systems SMARTExistence of monitoring programs concerning pollutants inputs UNEP-MAPNational regulations on wastewater SMART

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3) SUSTAINABILITY INDICATORS

IMPACT INDICATORS:

D/S ratio for agriculture SMART % WaterWare

or: GDP from agriculture SMARTthousands euros

D/S ratio for industry SMART % WaterWare

or: GDP from industry SMARTthousands euros

D/S ratio for tourism SMART % WaterWare

or: GDP from turistic sector SMARTthousands euros

Economic efficiency of the system SMARTeuros/ mc H2O WaterWare

IMPACT INDICATORS:D/S ratio for domestic uses SMART % WaterWareor: number of days without drinking water SMART days/year WaterWare

IMPACT INDICATOR:D/S ratio for environmental uses SMART %

STATE INDICATORS: Nutrients in coastal waters EEA Telemac

Hazardous substances in transitional, coastal and marine waters EEA Telemac

or: Global quality of coastal waters UNEP - MAP class (I-IV) Telemac

or: Bathing water quality EEA class (I-IV) Telemac

PRESSURE INDICATOR: Water exploitation index (WEI) EEA and

UNEP-MAP: Mean annual total abstraction of freshwater / long-term average freshwater

%

CROSSCUTTING

SOURCE

ECONOMIC

SOCIAL

ENVIRONMENTAL

TYPE INDICATORPROPOSED

BY:PROPOSED

UNIT

UATLApresentation

SOGREAHpresentation